U.S. patent number 8,631,080 [Application Number 12/402,735] was granted by the patent office on 2014-01-14 for email characterization.
This patent grant is currently assigned to Microsoft Corporation. The grantee listed for this patent is John R. Burkhardt, Barnaby M. Claydon, Robert F. Goodman, Michael R. Gretzinger, Katherine W. Rae, Rachel R. Schiff, Reed P. Sturtevant. Invention is credited to John R. Burkhardt, Barnaby M. Claydon, Robert F. Goodman, Michael R. Gretzinger, Katherine W. Rae, Rachel R. Schiff, Reed P. Sturtevant.
United States Patent |
8,631,080 |
Goodman , et al. |
January 14, 2014 |
Email characterization
Abstract
Email users may feel overwhelmed with the abundance of emails
they receive. Many current email management techniques require at
least some manual intervention that may be time consuming and/or
otherwise frustrating to a user. As provided herein, emails may be
characterized based upon content of the email and domain
classification data (e.g., a company name, business category, or a
website name associated with the domain name of the sender). One or
more viewing panels may be populated with the characterized emails.
A viewing panel may present emails corresponding to a particular
characterization (e.g., a shopping viewing panel may display emails
characterized as shopping). To enhance characterization, rules used
to characterize emails may learn from a user's actions. For
example, a user may move an email from a shopping viewing panel to
a travel viewing panel, thus altering the characterization of the
email from shopping to travel.
Inventors: |
Goodman; Robert F. (Allston,
MA), Gretzinger; Michael R. (Malden, MA), Burkhardt; John
R. (Arlington, MA), Schiff; Rachel R. (Cambridge,
MA), Claydon; Barnaby M. (Hingham, MA), Rae; Katherine
W. (Brookline, MA), Sturtevant; Reed P. (Lexington,
MA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Goodman; Robert F.
Gretzinger; Michael R.
Burkhardt; John R.
Schiff; Rachel R.
Claydon; Barnaby M.
Rae; Katherine W.
Sturtevant; Reed P. |
Allston
Malden
Arlington
Cambridge
Hingham
Brookline
Lexington |
MA
MA
MA
MA
MA
MA
MA |
US
US
US
US
US
US
US |
|
|
Assignee: |
Microsoft Corporation (Redmond,
WA)
|
Family
ID: |
42731555 |
Appl.
No.: |
12/402,735 |
Filed: |
March 12, 2009 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20100235447 A1 |
Sep 16, 2010 |
|
Current U.S.
Class: |
709/206; 709/225;
709/207; 709/246; 709/238; 709/242 |
Current CPC
Class: |
G06Q
10/107 (20130101) |
Current International
Class: |
G06F
15/00 (20060101) |
Field of
Search: |
;709/206,207,225,238,242,246 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Ramaraj, et al., "Automated Classification of Customer Emails via
Association Rule Mining", Retrieved at
<<http://www.scialert.net/qredirect.php?doi=itj.2007.567.572&linkid-
=pdf>>, Information Technology Journal 6(4), 2007, pp.
567-572. cited by applicant .
Cignini, et al., "E-Mail on the Move: Categorization, Filtering,
and Alerting on Mobile Devices with the ifMail Prototype",
Retrieved at
<<http://www.springerlink.com/content/9ewve0vp3e7xq6ft/fulltext.pdf-
>>, Mobile and Ubiquitous Info. Access Ws 2003, LNCS 2954,
2004, pp. 107-123. cited by applicant .
"Microsoft Outlook 2007 Managing Email with Folders", Retrieved at
<<http://lis.dickinson.edu/Technology/Training/Tutorials/ms2007/out-
look/outlook.sub.--folders.pdf>>, pp. 5. cited by applicant
.
Yang, et al., "Email Categorization Using Fast Machine Learning
Algorithms", Retrieved at
<<http://www.springerlink.com/content/cvjuxvrjl1qtwe4v/fulltext.pdf-
>>, DS 2002, LNCS 2534, 2002, pp. 316-323. cited by applicant
.
Xia, et al., "An Agent for Semi-automatic Management of Emails",
http://www.cs.cityu.edu.hk/.about.liuwy/publications/EmailAgent.sub.--APC-
HI.pdf, Dept. of Computer Science & Technology, Tsinghua
University, Beijing 100084, China Dept. of Computer Science, City
University of Hong Kong, Hong Kong SAR, China, pp. 1-8. cited by
applicant .
Fong, Philip W.L., "Preventing Sybil Attacks by Privilege
Attenuation: A Design Principle for Social Network Systems",
Retrieved at
<<http://pages.cpsc.ucalgary.ca/.about.pwlfong/Pub/sp2011.pdf>&g-
t;, Dec. 1, 2010, pp. 16. cited by applicant .
Xu, et al., "Resisting Sybil Attack by Social Network and Network
Clustering", Retrieved at
<<http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5598178&g-
t;>, Proceedings of the 10th Annual International Symposium on
Applications and the Internet, Jul. 19-23, 2010, p. 15-21. cited by
applicant .
Wondracek, et al., "A Practical Attack to De-Anonymize Social
Network Users", Retrieved at
<<http://www.iseclab.org/papers/sonda-tr.pdf>>, Feb. 1,
2011, pp. 15. cited by applicant .
Mislove, et al., "Ostra: Leveraging trust to thwart unwanted
communication", Retrieved at
<<http://www.mpi-sws.org/.about.gummadi/papers/ostra.pdf>>,
Oct. 15, 2007, pp. 16. cited by applicant .
Leung, et al., "Implementation of a Focused Social Networking
Crawler", Retrieved at
<<http://courses.ece.ubc.ca/412/term.sub.--project/reports/2009/foc-
used.sub.--social.sub.--net.sub.--crawler.pdf>>, Retrieved
Date: Apr. 14, 2011, pp. 1-6. cited by applicant .
Ayodele, et al., "Email Classification and Summarization: A Machine
Learning Approach", Retrieved at <<
http://userweb.port.ac.uk/.about.khusainr/papers/ccwmsn07.sub.--taiwo.pdf
>>, IET Conference on Wireless, Mobile and Sensor Networks,
Dec. 12-4, 2007, pp. 5. cited by applicant .
Martin, et al., "Analyzing Behaviorial Features for Email
Classification", Retrieved at <<
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.5285&rep=rep1-
&type=pdf >>,vol. 3 No. 2, Jul. 21, 2005, pp. 8. cited by
applicant .
Segal, et al., "MailCat: An Intelligent Assistant for Organizing
E-Mail", Retrieved at <<
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.65.7439&rep=rep1-
&type=pdf >>, In Proceedings of the Third International
Conference on Autonomous Agents, vol. 22 No. 4, 1999, pp. 8. cited
by applicant .
"G-Lock Email Processor 1.96", Retrieved at <<
http://www.freedownloadmanager.org/downloads/parse-incoming-mime-email-83-
5962.html >>, Nov. 10, 2005, pp. 3. cited by applicant .
Guan, et al., "Anomaly Based Malicious URL Detection in Instant
Messaging", Retrieved at
<<http://jwis2009.nsysu.edu.tw/location/paper/Anomaly%20Based%20Mal-
icious%20URL%20Detection%20in%20Instant%20Messaging. pdf>>,
The Fourth Joint Workshop on Information Security(JWIS), Aug. 6-7,
2009, pp. 1-14. cited by applicant .
Gianvecchio, et al., "Measurement and Classification of Humans and
Bots in Internet Chat", Retrieved at
<<http://www.cs.wm.edu/.about.hnw/paper/security08.pdf>>,
Proceedings of the 17th conference on Security symposium, 2008, pp.
15. cited by applicant .
Bi, et al., "A Trust and Reputation based Anti-SPIM Method",
Retrieved at
<<http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4509914&g-
t;>, IEEE INFOCOM. The 27th Conference on Computer
Communications. Apr. 13-18, 2008, pp. 371-375. cited by applicant
.
Trivedi, et al., "Analyzing Network and Content Characteristics of
SPIM using Honeypots", Retrieved at
<<http://www.trustedsource.org/download/research.sub.--publications-
/trivedi.pdf>>, Proceedings of the 3rd USENIX workshop on
Steps to reducing unwanted traffic on the internet, 2007, pp. 9.
cited by applicant .
Benevenuto, et al., "Detecting Spammers on Twitter", Retrieved at
<<http://ceas.cc/2010/papers/Paper%2021.pdf>>, Seventh
Annual Collaboration Electronic Messaging, Anti-Abuse and Spam
Conference, Jul. 13-14, 2010, pp. 9. cited by applicant .
Xie, et al., "HoneyIM: Fast Detection and Suppression of Instant
Messaging Malware in Enterprise-like Networks", Retrieved at
<<http://www.foo.be/cours/dess-20072008/papers/154.pdf>>,
Dec. 10-14, 2007, pp. 10. cited by applicant .
Mayes, Brandon David, "Defending Against Malware in Online Social
Networks", Retrieved at
<<http://repository.lib.ncsu.edu/ir/bitstream/1840.16/6400/1/etd.pd-
f>>, 2010, pp. 65. cited by applicant .
Aimeur, et al., "Towards a Privacy-enhanced Social Networking
Site", Retrieved at
<<http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5438097&g-
t;>, International Conference on Availability, Reliability and
Security, Feb. 15-18, 2010, p. 172-179. cited by applicant .
"Improved network security with IP and DNS reputation"--Published
Date: Jul. 2010
http://resources.idgenterprise.com/original/AST-0007839.sub.--I-
P.sub.--and.sub.--DNS.sub.--reputation.sub.--white.sub.--paper.sub.---.sub-
.--4AA2-3535ENW.pdf. cited by applicant .
Kwan "Trend Micro.TM. Smart Protection Network Web Reputation
Service Architectural Overview"--Published Date: Nov. 2009
http://trendedge.trendmicro.com/pr/tm/te/document/SPN.sub.--and.sub.--WRS-
.sub.--Architecture.sub.--Overview.sub.--091112. pdf. cited by
applicant .
"Spam and Malware Protection" --Retrieved Date: Apr. 18, 2011
http://blog.bit.ly/post/263859706/spam-and-malware-protection.
cited by applicant .
"ThreatWatch.TM. IP Reputation Service from Security
On-Demand"--Retrieved Date: Apr. 18, 2011
http://www.securityondemand.com/main/SolutionCenter/ThreatWatch.htm.
cited by applicant .
"ipTrust Unveils New IP Reputation Intelligence Service"--Retrieved
Date: Apr. 18, 2011
http://www.securityweekcom/iptrust-unveils-new-ip-reputation-intelligence-
-service. cited by applicant .
Costa, et al., "Extending Security-by-Contract with Quantitative
Trust on Mobile Devices"--Published Date: Feb. 15-18, 2010
http://hal.archives-ouvertes.fr/docs/00/53/67/05/PDF/IMIS10.pdf.
cited by applicant .
Qian, et al., "Ensemble: Community-based anomaly detection for
popular applications"--Published Date: Sep. 14-18, 2009
http://www.cse.umich.edu/.about.zmao/Papers/ensemble.pdf. cited by
applicant .
"Bridging the Gap between Data-Flow and Control-Flow Analysis for
Anomaly Detection."--Published Date: Dec. 8-12, 2008
http://ieeexplore.ieee.org/stamp/samp.jsp?tp=&arnumber=4721575.
cited by applicant .
"Intrusion Detection via Static Analysis"--Published Date: May
14-16, 2001
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=924296.
cited by applicant .
"IE8 Security Part III: SmartScreen.RTM. Filter"--Retrieved Date:
Apr. 15, 2011
http:/blogsmsdncombiearchive/2008/07/02/ie8-security-part-iii-smarts-
creen-filter.aspx. cited by applicant .
Non-Final Office Action U.S. Appl. No. 13/110,202 dated Mar. 15,
2013, 23 pgs. cited by applicant .
Reply Non-Final Office Action U.S. Appl. No. 13/110,202 dated Jun.
17, 2013, 13 pgs. cited by applicant .
Non-Final Office Action U.S. Appl. No. 13/180,877 dated Apr. 1,
2013, 23 pgs. cited by applicant .
Reply Non-Final Office Action U.S. Appl. No. 13/180,877 dated Jul.
1, 2013, 16 pgs. cited by applicant .
Non-Final Office Action U.S. Appl. No. 13/110,174 dated Oct. 9,
2012, 13 pgs. cited by applicant .
Reply Non-Final Office Action U.S. Appl. No. 13/110,174 dated Jan.
9, 2013, 12 pgs. cited by applicant .
Final Office Action U.S. Appl. No. 13/110,174 dated May 10, 2013,
13 pgs. cited by applicant .
Non-Final Office Action U.S. Appl. No. 13/195,245 dated Dec. 28,
2012, 35 pgs. cited by applicant .
Reply Non-Final Office Action U.S. Appl. No. 13/195,245 dated Mar.
28, 2013, 13 pgs. cited by applicant .
Final Office Action U.S. Appl. No. 13/195,245 dated May 10, 2013,
41 pgs. cited by applicant .
Non-Final Office Action U.S. Appl. No. 13/180,838 dated Jun. 18,
2013, 30 pgs. cited by applicant .
Wikipedia "Uniform Resource Locator--Wikipedia, the free
encyclopedia", Jul. 14, 2011, reprinted from the Internet at:
http://enwikipedia.org, 4 pgs. cited by applicant .
Tyson Macaulay, Updated Aug. 2012, reprinted from the Internet at:
http://www.tysonmacaulay.com/m 47 pgs. cited by applicant .
Reply Final Office Action U.S. Appl. No. 13/110,174 dated Aug. 12,
2013, 14 pgs. cited by applicant .
Reply Final Office Action U.S. Appl. No. 13/195,245 dated Aug. 12,
2013, 16 pgs. cited by applicant .
Final Office Action U.S. Appl. No. 13/110,202 dated Oct. 10, 2013,
20 pgs. cited by applicant .
Final Office Action U.S. Appl. No. 13/180,877 dated Oct. 24, 2013,
24 pgs. cited by applicant .
Non-Final Office Action U.S. Appl. No. 13/195,245 dated Sep. 13,
2013, 52 pgs. cited by applicant .
Reply Non-Final Office Action U.S. Appl. No. 13/180,838 dated Sep.
18, 2013, 12 pgs. cited by applicant .
Final Office Action U.S. Appl. No. 13/180,838 dated Oct. 16, 2013,
29 pgs. cited by applicant.
|
Primary Examiner: Dharia; Rupal
Assistant Examiner: Nguyen; Van Kim T
Attorney, Agent or Firm: Microsoft Corporation
Claims
What is claimed is:
1. A method for characterizing emails comprising: extracting
non-user tagged email content and a domain identifier from an
email, the domain identifier merely comprising at least some of a
domain of a sender of the email; in response to sending the domain
identifier extracted from the email to a domain directory service,
receiving business information, from the domain directory service,
that comprises at least one of a business name associated with the
domain identifier, a business website associated with the domain
identifier, or a business category associated with the domain
identifier; determining a domain classification associated with the
email based upon the business information; characterizing the email
with a first characterization based upon executing a pattern
matching rule set upon the extracted email content and the domain
classification, the pattern matching rule set comprising a matching
algorithm not predefined by the sender of the email; extracting
second non-user tagged email content and a second domain identifier
from a second email, the second domain identifier merely comprising
at least some of a second domain of a second sender of the second
email; in response to sending the second domain identifier
extracted from the second email to a second domain directory
service, receiving second business information, from the second
domain directory service, that comprises at least one of a second
business name associated with the second domain identifier, a
second business website associated with the second domain
identifier, or a second business category associated with the
second domain identifier; determining a second domain
classification associated with the second email based upon the
second business information; characterizing the second email with a
second characterization based upon executing a second pattern
matching rule set upon the second extracted email content and the
second domain classification, the second pattern matching rule set
comprising a second matching algorithm not predefined by the second
sender of the second email; populating a viewing panel with at
least an indication of the email based upon the first
characterization; populating merely the viewing panel with at least
one of supplemental content or supplemental formatting based upon
the first characterization but not populating one or more other
viewing panels characterized with one or more other
characterizations not comprising the first characterization with at
least one of the supplemental content or the supplemental
formatting; populating a second viewing panel with at least an
indication of the second email based upon the second
characterization; populating merely the second viewing panel with
at least one of second supplemental content or second supplemental
formatting based upon the second characterization but not
populating one or more other viewing panels characterized with one
or more other characterizations not comprising the second
characterization with at least one of the second supplemental
content or the second supplemental formatting; and updating one or
more rules of at least one of the pattern matching rule set or the
second pattern matching rule set based upon a user dragging and
dropping at least one of: at least a representation of the email
from the viewing panel to a viewing panel other than the viewing
panel; or at least a representation of the second email from the
second viewing panel to a viewing panel other than the second
viewing panel, at least some of the method implemented at least in
part via a processing unit.
2. The method of claim 1, at least one of the supplemental
formatting or the second supplemental formatting comprising
highlighting.
3. The method of claim 1, at least one of the supplemental content
or the second supplemental content comprising a map.
4. The method of claim 1, comprising executing a bulk user command
upon one or more emails within at least one of the viewing panel or
the second viewing panel.
5. The method of claim 1, at least one of the extracted email
content or the second extracted email content comprising at least
one of: a sender name; a subject line; or message text.
6. The method of claim 1, comprising filtering one or more emails
within at least one of the viewing panel or the second viewing
panel based upon a filter.
7. The method of claim 1, comprising: creating one or more domain
identifier variations based upon at least one of the domain
identifier or the second domain identifier; and sending the one or
more domain identifier variations to at least one of the domain
directory service or the second domain directory service resulting
in at least one of the received business information or the
received second business information.
8. A system comprising: one or more processing units; and memory
comprising instructions that when executed by at least some of the
one or more processing units, perform a method for characterizing
emails comprising: extracting email content and a domain identifier
from an email; in response to sending the domain identifier
extracted from the email to a domain directory service, receiving
business information, from the domain directory service;
determining a domain classification associated with the email based
upon the business information; characterizing the email with a
first characterization based upon executing a pattern matching rule
set upon the extracted email content and the domain classification;
extracting second email content and a second domain identifier from
a second email; in response to sending the second domain identifier
extracted from the second email to a second domain directory
service, receiving second business information, from the second
domain directory service; determining a second domain
classification associated with the second email based upon the
second business information; characterizing the second email with a
second characterization based upon executing a second pattern
matching rule set upon the second extracted email content and the
second domain classification; populating a viewing panel with at
least an indication of the email based upon the first
characterization; populating merely the viewing panel with at least
one of supplemental content or supplemental formatting based upon
the first characterization but not populating one or more other
viewing panels characterized with one or more other
characterizations not comprising the first characterization with at
least one of the supplemental content or the supplemental
formatting; populating a second viewing panel with at least an
indication of the second email based upon the second
characterization; populating merely the second viewing panel with
at least one of second supplemental content or second supplemental
formatting based upon the second characterization but not
populating one or more other viewing panels characterized with one
or more other characterizations not comprising the second
characterization with at least one of the second supplemental
content or the second supplemental formatting; and updating one or
more rules of at least one of the pattern matching rule set or the
second pattern matching rule set based upon a user dragging and
dropping at least one of: at least a representation of the email
from the viewing panel to a viewing panel other than the viewing
panel; or at least a representation of the second email from the
second viewing panel to a viewing panel other than the second
viewing panel.
9. The system of claim 8, at least one of the supplemental
formatting or the second supplemental formatting comprising
highlighting.
10. The system of claim 8, at least one of the supplemental content
or the second supplemental content comprising a map.
11. The system of claim 8, the method comprising executing a bulk
user command upon one or more emails within at least one of the
viewing panel or the second viewing panel.
12. The system of claim 8, at least one of the extracted email
content or the second extracted email content comprising at least
one of: a sender name; a subject line; or message text.
13. The system of claim 8, the method comprising filtering one or
more emails within at least one of the viewing panel or the second
viewing panel based upon a filter.
14. The system of claim 8, the method comprising: creating one or
more domain identifier variations based upon at least one of the
domain identifier or the second domain identifier; and sending the
one or more domain identifier variations to at least one of the
domain directory service or the second domain directory service
resulting in at least one of the received business information or
the received second business information.
15. A computer readable storage memory excluding signals comprising
instructions that when executed, perform a method for
characterizing emails comprising: extracting email content and a
domain identifier from an email; in response to sending the domain
identifier extracted from the email to a domain directory service,
receiving business information, from the domain directory service;
determining a domain classification associated with the email based
upon the business information; characterizing the email with a
first characterization based upon executing a pattern matching rule
set upon the extracted email content and the domain classification;
extracting second email content and a second domain identifier from
a second email; in response to sending the second domain identifier
extracted from the second email to a second domain directory
service, receiving second business information, from the second
domain directory service; determining a second domain
classification associated with the second email based upon the
second business information; characterizing the second email with a
second characterization based upon executing a second pattern
matching rule set upon the second extracted email content and the
second domain classification; populating a viewing panel with at
least an indication of the email based upon the first
characterization; populating merely the viewing panel with at least
one of supplemental content or supplemental formatting based upon
the first characterization but not populating one or more other
viewing panels characterized with one or more other
characterizations not comprising the first characterization with at
least one of the supplemental content or the supplemental
formatting; populating a second viewing panel with at least an
indication of the second email based upon the second
characterization; populating merely the second viewing panel with
at least one of second supplemental content or second supplemental
formatting based upon the second characterization but not
populating one or more other viewing panels characterized with one
or more other characterizations not comprising the second
characterization with at least one of the second supplemental
content or the second supplemental formatting; and updating one or
more rules of at least one of the pattern matching rule set or the
second pattern matching rule set based upon a user dragging and
dropping at least one of: at least a representation of the email
from the viewing panel to a viewing panel other than the viewing
panel; or at least a representation of the second email from the
second viewing panel to a viewing panel other than the second
viewing panel.
16. A computer readable storage memory excluding signals of claim
15, at least one of the supplemental formatting or the second
supplemental formatting comprising highlighting.
17. A computer readable storage memory excluding signals of claim
15, at least one of the supplemental content or the second
supplemental content comprising a map.
18. A computer readable storage memory excluding signals of claim
15, the method comprising executing a bulk user command upon one or
more emails within at least one of the viewing panel or the second
viewing panel.
19. A computer readable storage memory excluding signals of claim
15, at least one of the extracted email content or the second
extracted email content comprising at least one of: a sender name;
a subject line; or message text.
20. A computer readable storage memory excluding signals of claim
15, the method comprising filtering one or more emails within at
least one of the viewing panel or the second viewing panel based
upon a filter.
Description
BACKGROUND
Email users may feel overwhelmed by the amount of email they
receive, and particularly by commercial emails that may come
repeatedly from a sender. For example, an email user may sign up
for weekly newsletters, social networking alerts, email purchase
receipts, and/or other emails. Many current email clients that host
email viewing user interfaces may contribute to the overload of
emails by presenting emails in an uncategorized linear manner
(e.g., organize by date received). Some email clients may allow
rules to be manually setup to provide some organization; however
manual setup is generally time consuming and/or otherwise
frustrating to email users.
SUMMARY
This Summary is provided to introduce a selection of concepts in a
simplified form that are further described below in the Detailed
Description. This Summary is not intended to identify key factors
or essential features of the claimed subject matter, nor is it
intended to be used to limit the scope of the claimed subject
matter.
A technique for characterizing emails is disclosed herein. Email
content and a domain identifier may be extracted from an email. The
domain identifier may be used to determine a domain classification.
In one example, the domain identifier may be used to query a domain
directory service for domain classification data (e.g., a company
name, a business category, a site name, a domain name description,
etc.) associated with the domain identifier. The email may be
characterized based upon the extracted email content and the domain
classification. In one example, a pattern matching rule set may be
executed upon the extracted email content and/or domain
classification to determine a characterization which may be
associated with the email. For example, an email may be
characterized as "travel" based upon extracted email content (e.g.,
a subject line comprising the text "cruise") and a domain
classification (e.g., "Travel Shop" company name, "vacations"
business category, etc.).
A viewing panel having particular characterization related
properties may be populated with an email based upon a
characterization of the email. For example, a travel viewing panel
may be populated with emails having a travel characteristic.
Multiple viewing panels may be presented within a single
environment. Respective viewing panels may display emails in a
particular format based upon their characterization. For example, a
travel email may be presented along with personal travel history,
hot vacation suggestions, and/or departure and arrival times
highlighted within the travel email. Within a viewing panel, bulk
user commands may be executed upon multiple emails based upon a
variety of conditions (e.g., delete all email from a particular
sender). A pattern matching rule set (e.g., rules that may be
executed upon a domain classification and/or extracted email
content to determine a characterization) may be updated based upon
user actions (e.g., dragging and dropping an email from a first
viewing panel to a second viewing panel).
To the accomplishment of the foregoing and related ends, the
following description and annexed drawings set forth certain
illustrative aspects and implementations. These are indicative of
but a few of the various ways in which one or more aspects may be
employed. Other aspects, advantages, and novel features of the
disclosure will become apparent from the following detailed
description when considered in conjunction with the annexed
drawings.
DESCRIPTION OF THE DRAWINGS
FIG. 1 is a flow chart illustrating an exemplary method of
characterizing an email.
FIG. 2 is a component block diagram illustrating an exemplary
system for characterizing an email.
FIG. 3 is an illustration of an example of determining a domain
classification based upon a domain identifier.
FIG. 4 is an illustration of an example of characterizing an
email.
FIG. 5 is an illustration of an example of presenting one or more
viewing panels.
FIG. 6 is an illustration of an example of presenting one or more
viewing panels.
FIG. 7 is an illustration of an example of presenting one or more
viewing panels.
FIG. 8 is an illustration of an example of presenting one or more
viewing panels.
FIG. 9 is an illustration of an exemplary computer-readable medium
comprising processor-executable instructions configured to embody
one or more of the provisions set forth herein.
FIG. 10 illustrates an exemplary computing environment wherein one
or more of the provisions set forth herein may be implemented.
DETAILED DESCRIPTION
The claimed subject matter is now described with reference to the
drawings, wherein like reference numerals are used to refer to like
elements throughout. In the following description, for purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the claimed subject matter. It
may be evident, however, that the claimed subject matter may be
practiced without these specific details. In other instances,
structures and devices are illustrated in block diagram form in
order to facilitate describing the claimed subject matter.
Email has become a prevalent means of communication. Unfortunately,
the number of emails that a user receives can be overwhelming, and
while there are some techniques for dealing with an (over)abundance
of emails, these techniques require at least some manual
intervention, that may be time consuming and/or otherwise
frustrating to a user. For example, rules may be developed by a
user, certain words may be blacklisted, folders can be created into
which emails can be manually placed, etc.
Accordingly, as provided herein, a technique for characterizing an
email is disclosed. Among other things, a domain identifier of the
email is consulted and a domain classification is determined
therefrom. The email is then characterized based upon the domain
classification, among other things. It will be appreciated that
this has particular application to solicited emails (e.g., opt-in
bulk email, social networking email, mailing list email, commercial
email, etc.). That is, those emails that are received by a user as
a result of some commercial or other type of activity that the user
is involved in, such as sales confirmation emails, etc., where the
user has some interest in the emails but may not open or read them
for a certain period of time (as opposed to unsolicited "spam"
emails to which the user is significantly disinterested).
One embodiment of characterizing an email is illustrated by an
exemplary method 100 in FIG. 1. At 102, the method begins. At 104,
email content and a domain identifier may be extracted from an
email. This data may be extracted from a header, fields, email
text, metadata, and/other other data relating to the email. The
extracted email content may comprise information, such as a sender
name, a subject line and/or message text, for example.
At 106, a domain classification may be determined based upon the
domain identifier. The domain identifier may comprise information
relating to the domain name originating the email. Domain
identifier variations may be created from the domain identifier
because the extracted domain identifier may not directly correspond
to the actual domain name of the sender. For example, a third party
may have sent the email on behalf of the actual entity originating
the email. In another example, the domain identifier may reflect a
variation of the actual domain name of the entity sending the
email.
In one example of determining a domain classification, a domain
directory service (e.g., a database, a web service, an open
directory categorizing domain names with business information,
etc.) may be queried with the domain identifier and/or domain
identifier variations to determine the domain classification. The
domain classification may comprise a company name, a business
category, a canonical site name, a domain name description, and/or
other information corresponding to the domain identifier (e.g., a
registered domain name).
At 108, the email may be characterized based upon the extracted
email content and the domain classification. For example, a pattern
matching rule set (e.g., an algorithm configured to match domain
classifications and email content against patterns) may be executed
upon the domain classification and the extracted email content to
determine a characterization which may be assigned to or otherwise
associated with the email. It will be appreciated that one or more
characterizations (e.g., subcharacterizations) may likewise be
associated with an email. For example, a second pattern matching
rule set may be executed upon the extracted email content to
determine a subcharacterization which may be associated with the
email. It will be appreciated that a pattern matching rule set may
be updated (e.g. the pattern matching rule set may learn from a
user's actions) based upon user input. For example, a user may
execute a viewing panel email swap (e.g., dragging and dropping an
email from a first viewing panel to a second viewing panel) in
which a pattern matching rule set may be updated to reflect the
user's specified characterization for the particular email and/or
sender that was swapped.
A viewing panel having particular characterization related
properties with the email may be populated based upon the
characterization. For example, a shopping viewing panel may be
populated with an email characterized as shopping. It will be
appreciated that an email within a particular viewing panel may be
presented in a particular format based upon the characterization.
For example, an email within a travel viewing panel may be
presented with highlighted departure and arrival times within the
email and/or with additional travel information (e.g., a map)
(where a different viewing panel would not have these same
properties). It may be appreciated that entity extraction may also
be performed upon an email to extract additional information for
display within a viewing panel. For example, entity extract may be
performed upon text of an email to extract contextual information
(e.g., a street address, an order confirmation number, an
itinerary, a coupon amount, shipping information, etc.). The
contextual information may be presented in association with the
email and/or a particular viewing panel to provide an enriched view
of the email for the particular context.
Within the viewing panel, one or more emails may be filtered based
upon a filter. For example, all emails from a particular sender may
be minimized. In another example, all emails outside of a
particular date range (e.g., current month) may be minimized. Bulk
user commands may be executed upon one or more emails within a
viewing panel. For example, a user may delete all emails from a
particular sender with a single click. At 110, the method ends.
FIG. 2 illustrates an example 200 of a system configured for
characterizing emails. The system may comprise an extraction
component 204, a domain classification component 210, and/or a
characterization component 214. The system may also comprise a
presentation component 216 and/or a command execution component
220.
The extraction component 204 may be configured to extract email
content and a domain identifier 206 from an email 202. The domain
classification component 210 may be configured to determine a
domain classification based upon the domain identifier. The domain
classification may comprise information related to the domain name
from which the email was sent (e.g., business name of the sender,
business category of the sender, website name of the sender, etc.).
For example the domain classification component 210 may query a
domain directory service 208 with the domain identifier to
determine the domain classification. In another example the domain
classification component 210 may be configured to create domain
identifier variations based upon the domain identifier. The domain
identifier and/or domain identifier variations may be used to query
the domain directory service 208 to determine the domain
classification.
The characterization component 214 may be configured to
characterize the email 202 based upon the extracted email content
and the domain classification (e.g., email content and domain
classification 212). In one example, the characterization component
214 may be configured to execute a pattern matching rule set upon
the domain classification and the extracted email content to
determine the characterization which may be associated with the
email 202. The pattern matching rule set may execute one or more
pattern matching algorithms to match predefined patterns with the
domain classification and the extracted email content. It will be
appreciated that the characterization component 214 may be
configured to characterize the email 202 with one or more
characterizations (e.g., subcharacterizations).
The presentation component 216 may be configured to populate one or
more viewing panels (e.g., set of viewing panels 218) with
characterized emails. For example, the presentation component 216
may populate a viewing panel having a particular characterization
related property (e.g., a newsletter characterization viewing
panel) with an email (e.g., a newsletter email) based upon a
characterization (e.g., newsletter) of the email. The presentation
component 216 may be configured to present a characterized email
within a viewing panel in a particular format based upon a
characterization of the email and/or viewing panel. For example, an
email within a shopping viewing panel may be presented with
additional sales history and/or coupons, whereas an email within a
travel viewing panel may be presented with itinerary information
and/or a map.
The command execution component 220 may be configured to execute a
bulk user command upon one or more emails within a viewing panel.
For example, a user may delete all emails from a sender with a
single user input (e.g., single click). In another example, a user
may archive all emails from a sender with a single user input. In
yet another example, one or more emails and/or senders may be
swapped from a first viewing panel to a target viewing panel, in
this way a user may recharacterize the swapped emails/senders to a
characterization corresponding to the target viewing panel. The
pattern matching rule set may be updated 222 based upon the swap.
For example, patterns (e.g., predefined email content and/or domain
classification data) may be updated to reflect the
recharacterization.
FIG. 3 illustrates an example 300 of determining a domain
classification based upon a domain identifier. A domain
classification component 302 may query a domain directory service
306 with a domain identifier 304. The domain identifier 304 may
correspond to a domain name derived from normalizing extracted
content of an email. The domain directory service 306 may comprise
one or more domains (e.g., domain (1) 308, domain (2) 310, and
domain (n) 312) and their respective domain classification
information (e.g., company name, business category, canonical site
name, domain name description, etc.).
A domain classification 314 may be determined based upon the
queried domain identifier 304. For example, the domain identifier
304 may correspond to domain (1) 308 (e.g., a match in a domain
name is determined). The domain classification 314 may be returned
comprising the company name "Tom's book store", the business
category "Shopping", the canonical site name "Tom's book store
website", and/or the domain name description "Book seller". This
information may be used to determine a characterization (e.g.,
Shopping) and/or one or more subcharacterizations (e.g., Books)
corresponding to the email from which the domain identifier 304 was
extracted.
FIG. 4 illustrates an example 400 of characterizing an email. An
email 402 regarding a recent book purchase from Tom's books may be
received. Email content 404 may be extracted from the email 402.
For example, "Book" and "Order" may be extracted from the subject
and "Tom's Books" may be extracted from the message text. A domain
identifier 406 may be extracted from the email 402. For example,
"Tombooks" may be extracted from the sender address.
A domain classification component 408 may query a domain directory
service 410 with the domain identifier 406 to determine a domain
classification 412. For example, the domain name "Tombooks" may be
registered with the domain directory service 410. The registration
may provide additional information regarding the domain name
"Tombooks", such as a company name "Tom's book store", a business
category "Shopping", a site name "Tom's book store website", and/or
a domain name description "Book seller". The domain classification
412 may be determined based upon the additional information.
A characterization component 414 may execute a pattern matching
rule set 416 (e.g., an algorithm configured to compare extracted
email content and a domain classification with characterization
information within an XML file) upon the email content 404 and/or
the domain classification 412 to determine a characterization
and/or subcharacterization (e.g., characterization data 420). For
example, the business category "Shopping" may match a
characterization of "Shopping". Furthermore, "Shopping order" and
"Book store" may be derived as subcharacterizations further
describing the email 402. The characterization data 420 may be
associated with the email 402.
The characterization component 414 may also be configured to
perform entity extraction upon an email to extract additional
information for display within a viewing panel. For example, entity
extract may be performed upon text of an email to extract
contextual information (e.g., a street address, an order
confirmation number, an itinerary, coupon amounts, shipping
information, etc.). The contextual information may be presented in
association with the email and/or a particular viewing panel to
provide an enriched view of the email for the particular
context.
FIG. 5 illustrates an example 500 of presenting one or more viewing
panels. Within a computing environment 502, a shopping viewing
panel 510, a social viewing panel 522, a finance viewing panel 524,
and a newsletters viewing panel 526 may be presented. The computing
environment 502 may comprise email management utilities, such as a
date filter 504, a search filter 506, and/or a create new
characterization button 508, for example.
The shopping viewing panel 510 may have particular characterization
related properties relating to shopping. The shopping viewing panel
510 may present emails having a shopping characterization. For
example, a first Tom's Books email 514 and a second Tom's Books
email 516 may be presented under the Tom's Books company tab 512
within the shopping viewing panel 510 because the emails are
characterized as shopping and are associated with the shopping
company Tom's Books. The "(2)" next to the Tom's Books company tab
512 may indicate the number of emails associated with the shopping
company Tom's books. Other emails having a shopping
characterization, such as a first Jane's Clothing Store email 520,
may be displayed within the shopping viewing panel 510. The first
Jane's Clothing Store email 520 may be presented under a Jane's
Clothing Store company tab 518.
The social viewing panel 522 may comprise emails having a social
characterization. For example, the sender We Connect People may be
characterized as social; therefore emails associated with We
Connect People may be presented within the social viewing panel
522. The finance viewing panel 524 may comprise emails having a
finance characterization. For example, the sender Bank may be
characterized as financial; therefore emails associated with Bank
may be presented within the finance viewing panel 524. The
newsletters viewing panel 526 may comprise emails having a
newsletters characterization. A minimize button 530 may be
associated with the newsletters viewing panel 526. For example, a
user may perform a bulk minimize upon emails associated with
Healthy News. The Healthy News company tab 528 may be collapsed
upon the emails associated with Healthy News (e.g., the Healthy
News company tab 528 comprises 3 emails). It may be appreciated
that a viewing panel may comprise one or more emails and/or company
tabs corresponding to senders that may not be visually presented
due to filters, searching, minimizing, and/or other constraints,
for example.
The date filter 504 may be used to filter emails within one or more
viewing panels based upon a date range (e.g., this week, this
month, today, all). The search filter 506 may be used to filter
emails within one or more viewing panels based upon a textual
input. The create new characterization button 508 may be used to
create a new characterization and/or a viewing panel having the new
characterization. This provides flexibility in characterizing
emails. For example, if a new characterization and new
characterization viewing panel is created, then an email and/or
sender may be swapped into the new characterization viewing panel.
To adapt to the user's preference, a pattern matching rule set may
be updated and/or trained to characterize emails as the new
characterization.
FIG. 6 illustrates an example 600 of presenting one or more viewing
panels. Within a computing environment 602, a shopping viewing
panel, a social viewing panel, a finance viewing panel, and a
newsletters viewing panel may be presented. An archive button 604
and a delete button 606 may be associated with the social viewing
panel. The archive button 604 may be used to archive one or more
emails within the social viewing panel. The delete button may be
used to delete one or more emails within the social viewing panel.
For example, the delete button 606 may be invoked to delete 608 all
emails associated with the company We Connect People. This may
allow a user to efficiently manage their email by performing bulk
operations, such as delete. A maximize button 610 may be associated
with the newsletter viewing panel. The maximize button 610 may be
invoked to expand 612 a collapsed group of emails (e.g., three
emails associated with Healthy News). This may allow a user to
control the visual presentation of emails within viewing
panels.
FIG. 7 illustrates an example 700 of presenting one or more viewing
panels. Within a computing environment 702, a shopping viewing
panel, a social viewing panel, a finance viewing panel 704, and a
newsletters viewing panel 706 may be presented. The newsletters
viewing panel 706 may be configured to present emails having a
newsletter characterization (e.g., email newsletters associated
with Investing 101, email newsletters associated with Healthy News,
etc.). A user may determine that emails from Investing 101 are more
appropriately characterized as finance emails. The user may perform
a view panel email swap 710, where the emails associated with
Investing 101 may be dragged and dropped 712 into the finance
viewing panel 704. This allows a user to recharacterize emails
and/or senders (e.g., companies) to more appropriate
characterization. A pattern matching rule set that may be utilized
in characterizing emails may be updated based upon the viewing
panel email swap 710, thus future emails from Investing 101 may be
characterized as finance.
FIG. 8 illustrates an example 800 of presenting one or more viewing
panels. Within a computing environment 802, a shopping viewing
panel 804 configured to present emails having a shopping
characterization and a travel viewing panel 812 configure to
present emails having a travel characterization may be presented.
The shopping viewing panel 804 may have properties associated with
the shopping characterization. For example, the shopping viewing
panel 804 may present a shopping email 806 in a particular format
based upon the shopping characterization. Shopping coupons 808 and
information regarding shopping trends 810 may be presented, for
example. The travel viewing panel 812 may have properties
associated with the travel characterization. For example, the
travel viewing panel 812 may present a travel email 814 in a
particular format based upon the travel characterization. Text may
be highlighted (e.g., itinerary text an 816 may be highlighted
within the message of the email 814) and/or additional information
may be presented (e.g., previous vacation data 820 and things to do
at the beach information 818).
Still another embodiment involves a computer-readable medium
comprising processor-executable instructions configured to
implement one or more of the techniques presented herein. An
exemplary computer-readable medium that may be devised in these
ways is illustrated in FIG. 9, wherein the implementation 900
comprises a computer-readable medium 908 (e.g., a CD-R, DVD-R, or a
platter of a hard disk drive), on which is encoded
computer-readable data 906. This computer-readable data 906 in turn
comprises a set of computer instructions 904 configured to operate
according to one or more of the principles set forth herein. In one
such embodiment 900, the set of computer instructions 904 may be
configured to perform a method 902, such as the exemplary method
100 of FIG. 1, for example. In another such embodiment, the set of
computer instructions 904 may be configured to implement a system,
such as the exemplary system 200 of FIG. 2, for example. Many such
computer-readable media may be devised by those of ordinary skill
in the art that are configured to operate in accordance with the
techniques presented herein.
Although the subject matter has been described in language specific
to structural features and/or methodological acts, it is to be
understood that the subject matter defined in the appended claims
is not necessarily limited to the specific features or acts
described above. Rather, the specific features and acts described
above are disclosed as example forms of implementing the
claims.
As used in this application, the terms "component," "module,"
"system", "interface", and the like are generally intended to refer
to a computer-related entity, either hardware, a combination of
hardware and software, software, or software in execution. For
example, a component may be, but is not limited to being, a process
running on a processor, a processor, an object, an executable, a
thread of execution, a program, and/or a computer. By way of
illustration, both an application running on a controller and the
controller can be a component. One or more components may reside
within a process and/or thread of execution and a component may be
localized on one computer and/or distributed between two or more
computers.
Furthermore, the claimed subject matter may be implemented as a
method, apparatus, or article of manufacture using standard
programming and/or engineering techniques to produce software,
firmware, hardware, or any combination thereof to control a
computer to implement the disclosed subject matter. The term
"article of manufacture" as used herein is intended to encompass a
computer program accessible from any computer-readable device,
carrier, or media. Of course, those skilled in the art will
recognize many modifications may be made to this configuration
without departing from the scope or spirit of the claimed subject
matter.
FIG. 10 and the following discussion provide a brief, general
description of a suitable computing environment to implement
embodiments of one or more of the provisions set forth herein. The
operating environment of FIG. 10 is only one example of a suitable
operating environment and is not intended to suggest any limitation
as to the scope of use or functionality of the operating
environment. Example computing devices include, but are not limited
to, personal computers, server computers, hand-held or laptop
devices, mobile devices (such as mobile phones, Personal Digital
Assistants (PDAs), media players, and the like), multiprocessor
systems, consumer electronics, mini computers, mainframe computers,
distributed computing environments that include any of the above
systems or devices, and the like.
Although not required, embodiments are described in the general
context of "computer readable instructions" being executed by one
or more computing devices. Computer readable instructions may be
distributed via computer readable media (discussed below). Computer
readable instructions may be implemented as program modules, such
as functions, objects, Application Programming Interfaces (APIs),
data structures, and the like, that perform particular tasks or
implement particular abstract data types. Typically, the
functionality of the computer readable instructions may be combined
or distributed as desired in various environments.
FIG. 10 illustrates an example of a system 1010 comprising a
computing device 1012 configured to implement one or more
embodiments provided herein. In one configuration, computing device
1012 includes at least one processing unit 1016 and memory 1018.
Depending on the exact configuration and type of computing device,
memory 1018 may be volatile (such as RAM, for example),
non-volatile (such as ROM, flash memory, etc., for example) or some
combination of the two. This configuration is illustrated in FIG.
10 by dashed line 1014.
In other embodiments, device 1012 may include additional features
and/or functionality. For example, device 1012 may also include
additional storage (e.g., removable and/or non-removable)
including, but not limited to, magnetic storage, optical storage,
and the like. Such additional storage is illustrated in FIG. 10 by
storage 1020. In one embodiment, computer readable instructions to
implement one or more embodiments provided herein may be in storage
1020. Storage 1020 may also store other computer readable
instructions to implement an operating system, an application
program, and the like. Computer readable instructions may be loaded
in memory 1018 for execution by processing unit 1016, for
example.
The term "computer readable media" as used herein includes computer
storage media. Computer storage media includes volatile and
nonvolatile, removable and non-removable media implemented in any
method or technology for storage of information such as computer
readable instructions or other data. Memory 1018 and storage 1020
are examples of computer storage media. Computer storage media
includes, but is not limited to, RAM, ROM, EEPROM, flash memory or
other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or
other optical storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or any other medium
which can be used to store the desired information and which can be
accessed by device 1012. Any such computer storage media may be
part of device 1012.
Device 1012 may also include communication connection(s) 1026 that
allows device 1012 to communicate with other devices. Communication
connection(s) 1026 may include, but is not limited to, a modem, a
Network Interface Card (NIC), an integrated network interface, a
radio frequency transmitter/receiver, an infrared port, a USB
connection, or other interfaces for connecting computing device
1012 to other computing devices. Communication connection(s) 1026
may include a wired connection or a wireless connection.
Communication connection(s) 1026 may transmit and/or receive
communication media.
The term "computer readable media" may include communication media.
Communication media typically embodies computer readable
instructions or other data in a "modulated data signal" such as a
carrier wave or other transport mechanism and includes any
information delivery media. The term "modulated data signal" may
include a signal that has one or more of its characteristics set or
changed in such a manner as to encode information in the
signal.
Device 1012 may include input device(s) 1024 such as keyboard,
mouse, pen, voice input device, touch input device, infrared
cameras, video input devices, and/or any other input device. Output
device(s) 1022 such as one or more displays, speakers, printers,
and/or any other output device may also be included in device 1012.
Input device(s) 1024 and output device(s) 1022 may be connected to
device 1012 via a wired connection, wireless connection, or any
combination thereof. In one embodiment, an input device or an
output device from another computing device may be used as input
device(s) 1024 or output device(s) 1022 for computing device
1012.
Components of computing device 1012 may be connected by various
interconnects, such as a bus. Such interconnects may include a
Peripheral Component Interconnect (PCI), such as PCI Express, a
Universal Serial Bus (USB), firewire (IEEE 1394), an optical bus
structure, and the like. In another embodiment, components of
computing device 1012 may be interconnected by a network. For
example, memory 1018 may be comprised of multiple physical memory
units located in different physical locations interconnected by a
network.
Those skilled in the art will realize that storage devices utilized
to store computer readable instructions may be distributed across a
network. For example, a computing device 1030 accessible via
network 1028 may store computer readable instructions to implement
one or more embodiments provided herein. Computing device 1012 may
access computing device 1030 and download a part or all of the
computer readable instructions for execution. Alternatively,
computing device 1012 may download pieces of the computer readable
instructions, as needed, or some instructions may be executed at
computing device 1012 and some at computing device 1030.
Various operations of embodiments are provided herein. In one
embodiment, one or more of the operations described may constitute
computer readable instructions stored on one or more computer
readable media, which if executed by a computing device, will cause
the computing device to perform the operations described. The order
in which some or all of the operations are described should not be
construed as to imply that these operations are necessarily order
dependent. Alternative ordering will be appreciated by one skilled
in the art having the benefit of this description. Further, it will
be understood that not all operations are necessarily present in
each embodiment provided herein.
Moreover, the word "exemplary" is used herein to mean serving as an
example, instance, or illustration. Any aspect or design described
herein as "exemplary" is not necessarily to be construed as
advantageous over other aspects or designs. Rather, use of the word
exemplary is intended to present concepts in a concrete fashion. As
used in this application, the term "or" is intended to mean an
inclusive "or" rather than an exclusive "or". That is, unless
specified otherwise, or clear from context, "X employs A or B" is
intended to mean any of the natural inclusive permutations. That
is, if X employs A; X employs B; or X employs both A and B, then "X
employs A or B" is satisfied under any of the foregoing instances.
In addition, the articles "a" and "an" as used in this application
and the appended claims may generally be construed to mean "one or
more" unless specified otherwise or clear from context to be
directed to a singular form.
Also, although the disclosure has been shown and described with
respect to one or more implementations, equivalent alterations and
modifications will occur to others skilled in the art based upon a
reading and understanding of this specification and the annexed
drawings. The disclosure includes all such modifications and
alterations and is limited only by the scope of the following
claims. In particular regard to the various functions performed by
the above described components (e.g., elements, resources, etc.),
the terms used to describe such components are intended to
correspond, unless otherwise indicated, to any component which
performs the specified function of the described component (e.g.,
that is functionally equivalent), even though not structurally
equivalent to the disclosed structure which performs the function
in the herein illustrated exemplary implementations of the
disclosure. In addition, while a particular feature of the
disclosure may have been disclosed with respect to only one of
several implementations, such features may be combined with one or
more other features of the other implementations as may be desired
and advantageous for any given or particular application.
Furthermore, to the extent that the terms "includes", "having",
"has", "with", or variants thereof are used in either the detailed
description or the claims, such terms are intended to be inclusive
in a manner similar to the term "comprising."
* * * * *
References